• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.57.1-57.1
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• 2011

It has been suggested that merging plays an important role in the formation and the evolution of early-type galaxies (ETGs). Optical-NIR color gradients of ETGs in high density environments are found to be less steep than those of ETGs in low density environments, hinting frequent merger activities in ETGs in high density environments. In order to examine if the flat color gradients are the result of dry mergers, we studied the relations between merging features, color gradient, and environments of 281 low redshift ETGs selected from Sloan Digital Sky Survey (SDSS) Stripe82. The sample contains 222 relaxed ETGs, 38 ETGs with tidal features, 10 galaxies with dust features and 11 galaxies with tidal and dust features, and Near Infrared (NIR) images are taken from UKIRT Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS). We find that r-K color gradients of field sample galaxies are steeper than those of sample ETGs within cluster environments. For the field sample galaxies, a relatively large number of galaxies with peculiar features contribute to the steeper color gradients, while the absence of these peculiar early-type galaxies make color gradients of the cluster sample galaxies intact. In high density environment, ETGs are already evolved and relaxed, resulting flat color gradients. However, in low density environments, a majority of ETGs undergone merging recently which makes the color gradients steep.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.83-92
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• 2019

Advertising signboards are designed for the visibility to affects recall and recognition of costumers. It is well know that the visibility from images is created by the value difference among colors. The research defines whether the background color combination of outdoor signboards is configured to maximize visibility, by a series of color value variation in complex commercial facilities. The subject of study is to examine how the visibility is made by the color combination since visibility cannot be obtained independently. Two steps of analysis were performed to confirm that the color composition of signboards was based on the color value difference. The first is to analyze that the entire colors of signboards are clearly categorized as different value groups. All components of colors, hue, value and chroma had been analyzed by color aesthetic measures to prove that the value variation has the only regularity and the principle of composition. The second step is a further verification with an ample amount of samples to determine whether series of signboards create a value altering pattern. The data for analysis is gained by colorimetric survey and the color data are used for exponentializing the degree of combining, which shows selective affinity between each pair colors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1414-1419
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• 2015

A method for detecting a skin region on the image is generally used to detect the color information. However, If saturation lowered, skin detection is difficult because hue information of the pixels is lost. So in this paper, we propose a method of correcting color of lower saturation of skin region images by the lighting. Color correction process of this method is saturation image acquisition and low-saturation region classification, segmentation, and the saturation of the split in the low saturation region extraction and color values, the color correction sequence. This method extracts the low saturation regions in the image and extract the color and saturation in the region and the surrounding region to produce a color similar to the original color. Therefore, the method of extracting the low saturation region should be correctly preceding. Because more accurate segmentation in the process of obtaining a low saturation regions, we use a multi-threshold method proposed Otsu in Hue values of the HSV color space, and create a binary image. Our experimental results for 170 portrait images show a possibility that the proposed method could be used efficiently preprocessing of skin color detection method, because the detection result of proposed method is 5.8% higher than not used it.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Korean Institute of Interior Design Journal
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• no.21
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• pp.121-128
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• 1999

The purpose of this study is to provide practical guide for the Preference of exterior design in Apartment. The data is collected through questionnaire survey including observations and interviews. The sample consists of 229. The data is analyzed by using SPSS-PC. The major findings are as follows. (1) Most of residents recognize the importance of exterior design of apartments. (2) Most of residents are satisfied with the exterior design such as building form, color, and material and not satisfies with environment of apartments. (3) The most preferred color of Apartment is white as primary color and green as secondary color. (4) The most preferred color pattern is vertical type as facade, horizontal type as the back wall, super graphic and geometrical type as the side wall. (5) The preferred exterior images are modern, simple, and unique style.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.808-811
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• 2004

A full-color AMOLED display with an RGBW color filter pattern has been fabricated. Displays with this format require about $^1/_2$ the power of analogous RGB displays. RGBW and RGB 2.16inch diagonal displays with average power consumptions of 180 mW and 340 mW, respectively, are demonstrated for a set of standard digital still camera images at a luminance of 100 cd/$m^2$. In both cases, a white-emitting AMOLED is used as the light source. The higher efficiency of the RGBW format results because a large fraction of a typical image can be represented as white, and the white sub-pixel in an RGBW AMOLED display is highly efficient because of the absence of any color filter. RGBW and RGB AMOLED displays have the same color gamut and, aside from the power consumption difference, are indistinguishable.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1012-1020
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• 2000

In this paper, This paper presents a new algorithm which is used for detecting and extracting human masks from a color still image. The regions where each pixel has a value of skin-color were extracted from the Cb and Cr images, after the tone of the color image is converted to YCbCr from. A morphological filter is used to eliminate noise in the resulting image. By scanning it in horizontal and vertical ways under ways under threshold value, first candidate section is chosen. If it is not a face, secondary candidate section is taken and is divided into two candidate sections. The proposed algorithm is not affected by the variation of illuminations, because it uses only Cb and Cr components in YCbCr color format. Moreover, the face recognition was possible regardless of the degree of shifting face, changed shape, various sizes of the face, and the quality of image.

• Journal of the Korean Society of Food Culture
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• v.20 no.2
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• pp.261-272
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• 2005

The color and its coordination should intensify the customer's memory and awareness in a brand website, through the consistent communication strategy by which a variety of brand identification in the offline could be expressed efficiently. We evaluated top 5 brand-valuable family restaurants in this study, how they made the best use of the website as a new communication channel, and how they constructed the brand identification by the coloring of a website. We found out that they employed colors with a dynamic and lilting feelings matching the concept of a family restaurant. In addition, A color scheme was well designed for the specific character of a brand, though web-safe colors were seldom employed. This report can be a guide to a corporation for the color and its coordination in the website, when existing brand images need to be intensified and enhanced, or when a new brand image need to be constructed.

• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.53-57
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• 2007

In this paper, a new content-based color image retrieval method is proposed, in which both the color content and the spatial relationship of image have been taken into account. In order to represent the spatial distribution information of image, a disorder matrix, which has the invariance to the rotation and translation of the image content, has been designed. This is based on multi-resolution color-spatial information. We present our algorithm in the following section, and then verified the search results with comparison to other methods, such as color histogram, wavelet histogram, correlogram and wavelet correlogram. Experimental results with various types of images show that the proposed method not only achieves a high image retrieval performance but also improve the retrieval precision.